Causality guided machine learning model on wetland CH4 emissions across global wetlands

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Fältnamn	Indikatorer	Metadata
000		07107naa a2200769 4500
001		oai:DiVA.org:uu-486964
003		SwePub
008		221025s2022 \| \|\|\|\|\|\|\|\|\|\|\|000 \|\|eng\|
024	7	a https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-4869642 URI
024	7	a https://doi.org/10.1016/j.agrformet.2022.1091152 DOI
040		a (SwePub)uu
041		a engb eng
042		9 SwePub
072	7	a ref2 swepub-contenttype
072	7	a art2 swepub-publicationtype
100	1	a Yuan, Kunxiaojiau Lawrence Berkeley Natl Lab, Climate Sci Dept, Climate & Ecosyst Sci Div, Berkeley, CA 94720 USA.4 aut
245	1 0	a Causality guided machine learning model on wetland CH4 emissions across global wetlands
264	1	b Elsevier,c 2022
338		a print2 rdacarrier
520		a Wetland CH4 emissions are among the most uncertain components of the global CH4 budget. The complex nature of wetland CH4 processes makes it challenging to identify causal relationships for improving our understanding and predictability of CH4 emissions. In this study, we used the flux measurements of CH4 from eddy covariance towers (30 sites from 4 wetlands types: bog, fen, marsh, and wet tundra) to construct a causality-constrained machine learning (ML) framework to explain the regulative factors and to capture CH4 emissions at sub -seasonal scale. We found that soil temperature is the dominant factor for CH4 emissions in all studied wetland types. Ecosystem respiration (CO2) and gross primary productivity exert controls at bog, fen, and marsh sites with lagged responses of days to weeks. Integrating these asynchronous environmental and biological causal relationships in predictive models significantly improved model performance. More importantly, modeled CH4 emissions differed by up to a factor of 4 under a +1C warming scenario when causality constraints were considered. These results highlight the significant role of causality in modeling wetland CH(4 )emissions especially under future warming conditions, while traditional data-driven ML models may reproduce observations for the wrong reasons. Our proposed causality-guided model could benefit predictive modeling, large-scale upscaling, data gap-filling, and surrogate modeling of wetland CH4 emissions within earth system land models.
650	7	a NATURVETENSKAPx Geovetenskap och miljövetenskapx Klimatforskning0 (SwePub)105012 hsv//swe
650	7	a NATURAL SCIENCESx Earth and Related Environmental Sciencesx Climate Research0 (SwePub)105012 hsv//eng
653		a Eddy covariance CH4 emission
653		a Wetlands
653		a Causal inference
653		a Machine learning
700	1	a Zhu, Qingu Lawrence Berkeley Natl Lab, Climate Sci Dept, Climate & Ecosyst Sci Div, Berkeley, CA 94720 USA.4 aut
700	1	a Li, Fau Lawrence Berkeley Natl Lab, Climate Sci Dept, Climate & Ecosyst Sci Div, Berkeley, CA 94720 USA.;Univ Wisconsin Madison, Dept Forest & Wildlife Ecol, Madison, WI USA.4 aut
700	1	a Riley, William J.u Lawrence Berkeley Natl Lab, Climate Sci Dept, Climate & Ecosyst Sci Div, Berkeley, CA 94720 USA.4 aut
700	1	a Torn, Margaretu Lawrence Berkeley Natl Lab, Climate Sci Dept, Climate & Ecosyst Sci Div, Berkeley, CA 94720 USA.4 aut
700	1	a Chu, Housenu Lawrence Berkeley Natl Lab, Climate Sci Dept, Climate & Ecosyst Sci Div, Berkeley, CA 94720 USA.4 aut
700	1	a McNicol, Gavinu Univ Illinois, Dept Earth & Environm Sci, Chicago, IL USA.4 aut
700	1	a Chen, Minu Univ Wisconsin Madison, Dept Forest & Wildlife Ecol, Madison, WI USA.4 aut
700	1	a Knox, Sarau Univ British Columbia, Dept Geog, Vancouver, BC, Canada.4 aut
700	1	a Delwiche, Kyleu Univ Calif Berkeley, Dept Environm Sci Policy & Management, Berkeley, CA USA.4 aut
700	1	a Wu, Huayiu Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & Re, Wuhan, Peoples R China.4 aut
700	1	a Baldocchi, Dennisu Univ Calif Berkeley, Dept Environm Sci Policy & Management, Berkeley, CA USA.4 aut
700	1	a Ma, Hongxuu Univ Calif Berkeley, Dept Geog, Berkeley, CA USA.4 aut
700	1	a Desai, Ankur R.u Univ Wisconsin Madison, Dept Atmospher & Ocean Sci, Madison, WI USA.4 aut
700	1	a Chen, Jiquanu Michigan State Univ, Dept Geog Environm & Spatial Sci, E Lansing, MI USA.4 aut
700	1	a Sachs, Torstenu GFZ German Res Ctr Geosci, Potsdam, Germany.4 aut
700	1	a Ueyama, Masahitou Osaka Prefecture Univ, Grad Sch Life & Environm Sci, Sakai, Japan.4 aut
700	1	a Sonnentag, Oliveru Univ Montreal, Dept Geog, Montreal, PQ, Canada.4 aut
700	1	a Helbig, Manuelu Dalhousie Univ, Dept Phys & Atmospher Sci, Halifax, NS, Canada.4 aut
700	1	a Tuittila, Eeva-Stiinau Univ Eastern Finland, Sch Forest Sci, Joesnuu, Finland.4 aut
700	1	a Jurasinski, Geraldu Univ Rostock, Landscape Ecol, Rostock, Germany.4 aut
700	1	a Koebsch, Franziskau Univ Gottingen, Digital Forest, Gottingen, Germany.4 aut
700	1	a Campbell, Davidu Univ Waikato, Sch Sci, Hamilton, New Zealand.4 aut
700	1	a Schmid, Hans Peteru Karlsruhe Inst Technol, Inst Meteorol & Climate Res, Karlsruhe, Germany.4 aut
700	1	a Lohila, Annaleau Univ Helsinki, Inst Atmospher & Earth Syst Res Forest Sci, Helsinki, Finland.4 aut
700	1	a Goeckede, Mathiasu Max Planck Inst Biogeochem, Dept Biogeochem Signals, Jena, Germany.4 aut
700	1	a Nilsson, Mats B.u Swedish Univ Agr Sci, Dept Forest Ecol & Management, Umeå, Sweden.4 aut
700	1	a Friborg, Thomasu Univ Copenhagen, Dept Geosci & Nat Resource Management, Copenhagen, Denmark.4 aut
700	1	a Jansen, Joachim,d 1989-u Uppsala universitet,Institutionen för ekologi och genetik4 aut0 (Swepub:uu)joaja327
700	1	a Zona, Donatellau San Diego State Univ, Dept Biol, San Diego, CA USA.4 aut
700	1	a Euskirchen, Eugenieu Univ Alaska Fairbanks, Inst Arctic Biol, Fairbanks, AK USA.4 aut
700	1	a Ward, Eric J.u US Geol Survey, Wetland & Aquat Res Ctr, Lafayette, LA USA.4 aut
700	1	a Bohrer, Gilu Ohio State Univ, Dept Civil Environm & Geodet Engn, Columbus, OH USA.4 aut
700	1	a Jin, Zhenongu Univ Minnesota, Dept Bioprod & Biosyst Engn, St Paul, MN USA.4 aut
700	1	a Liu, Lichengu Univ Minnesota, Dept Bioprod & Biosyst Engn, St Paul, MN USA.4 aut
700	1	a Iwata, Hirokiu Shinshu Univ, Fac Sci, Dept Environm Sci, Matsumoto, Japan.4 aut
700	1	a Goodrich, Jordanu Univ Waikato, Sch Sci, Hamilton, New Zealand.4 aut
700	1	a Jackson, Robertu Stanford Univ, Dept Earth Syst Sci, Stanford, CA USA.4 aut
710	2	a Lawrence Berkeley Natl Lab, Climate Sci Dept, Climate & Ecosyst Sci Div, Berkeley, CA 94720 USA.b Lawrence Berkeley Natl Lab, Climate Sci Dept, Climate & Ecosyst Sci Div, Berkeley, CA 94720 USA.;Univ Wisconsin Madison, Dept Forest & Wildlife Ecol, Madison, WI USA.4 org
773	0	t Agricultural and Forest Meteorologyd : Elsevierg 324q 324x 0168-1923x 1873-2240
856	4 8	u https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-486964
856	4 8	u https://doi.org/10.1016/j.agrformet.2022.109115

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